Dask.distributed has limitations. Understanding these can help you to reliably create efficient distributed computations.
The central scheduler spends a few hundred microseconds on every task. For optimal performance, task durations should be greater than 10-100ms.
Dask can not parallelize within individual tasks. Individual tasks should be a comfortable size so as not to overwhelm any particular worker.
Dask assigns tasks to workers heuristically. It usually makes the right decision, but non-optimal situations do occur.
The workers are just Python processes, and inherit all capabilities and limitations of Python. They do not bound or limit themselves in any way. In production you may wish to run Dask workers within containers.
Assumptions on Functions and Data¶
Dask assumes the following about your functions and your data:
All functions must be serializable either with pickle or cloudpickle. This is usually the case except in fairly exotic situations. The following should work:
from cloudpickle import dumps, loads loads(dumps(my_object))
All data must be serializable either with pickle, cloudpickle, or using Dask’s custom serialization system.
Dask may run your functions multiple times, such as if a worker holding an intermediate result dies. Any side effects should be idempotent.
As a distributed computing framework, Dask enables the remote execution of arbitrary code. You should only host Dask workers within networks that you trust. This is standard among distributed computing frameworks, but is worth repeating.